Application of digital signal processing and machine learning for Electromyography: A review


  • Norhashimah Mohd Saad Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka
  • Ezreen Farina Shair Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka
  • Tengku Nor Shuhada Tengku Zawawi Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka



Digital Signal Processing, Machine Learning , Electromyography


This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

Author Biography


Technical University of Malaysia Malacca , Durian Tunggal , 76100, Melaka, Malaysia


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How to Cite

OMAR, S. N., Mohd Saad, N., Shair, E. F., & Tengku Zawawi, T. N. S. (2021). Application of digital signal processing and machine learning for Electromyography: A review. Asian Journal Of Medical Technology, 1(1), 30–45.